High-Intensity Drinking over the Life Course: Identifying High-Risk Subgroups through Event-based and Population-Based Approaches

摘要：高强度饮酒的生命历程：通过识别高风险亚组 基于事件和基于人群的方法 女性/男性在一个场合或一天内饮酒≥4/ ≥5杯的狂欢或重度间歇性饮酒（HED）是一种 在一般和临床人群中有害饮酒的关键标志物，并已广泛用于酒精 流行病学，以捕捉酒精使用的公共卫生负担。然而，批评者提出的问题， 阈值未能捕捉到高强度饮酒（HID）的影响，并意味着所有人的平均风险相同 不管他们喝了5杯还是15杯。最近的研究引起了人们对HID的关注 饮用两倍（8+/10+饮料）甚至三倍（12+/15+饮料）的HED量。随着每天的数量达到 极端水平，后果和酒精使用障碍（AUD）的风险大大增加，即使在平均水平 摄入量保持不变。这个拟议的中心项目将跟踪有害饮酒模式的趋势， 注意40年期间的HID发生率和比率，确定一系列与酒精有关的前体， 通过基于事件和基于人群的方法解决问题， 为高危人群的早期筛查和干预提供信息。我们将使用复杂的统计数据 方法检查协会和后果涉及HED和HID，使用两个成人一般 人口和两个重度饮酒者事件为基础的样本，每种类型的数据旨在产生 关于HID的补充和新知识。我们发扬ARG在酒精流行病学方面的丰富历史 和测量专业知识，以及该中心的使命，以减少酒精相关的差距，在我们所有的研究目标。 为了响应NIAAA的优先事项，并确保研究结果的直接转化影响，我们将 具体的理论和实践为每个研究目标。我们对HED/HID的定义包括阈值（5+，8+和 12+）和任何一天的饮酒量（从5-7，8-11，和12+饮料）。目标1从生命历程的角度出发， 记录从1984年到2024年的有害饮酒趋势，并按优先群体（例如，中年妇女 老年人;社会弱势群体，包括少数种族/族裔和教育程度较低的人; 和有AUD治疗史或正在康复的人）。目标2描述了提高 与HED水平相比，HID的风险。敏感性分析将比较是否调整饮酒 尺寸/强度和饮酒率（估计的BAC）将提高HID如何预测酒精问题的准确性（例如， AUD）。目的3将涉及HID的有害饮酒模式置于社会生态学模型中以检查风险 以及当前和整个生命过程中与HID密切相关的保护因素。关于Aim 我们评估了HID在多大程度上加重了AUD和其他问题的公共卫生负担， 使用风险模型和机器学习方法为所有饮酒者和优先群体提供HED和非HED水平。 研究结果将有助于确定关键人群亚组和风险/保护因素， 在事件、个人和环境层面制定有针对性的干预措施， 高强度饮酒及其相关后果。

ABSTRACT: High-Intensity Drinking over the Life Course: Identifying high-risk subgroups through event-based and population-based approaches Binge or heavy episodic drinking (HED) of ≥4/ ≥5 drinks for women/men in a single occasion or day has been a key marker of harmful drinking in general and clinical populations and has been widely used in alcohol epidemiology to capture the public health burden from alcohol use. Yet critics have raised the issue that the HED threshold fails to capture the impact of high-intensity drinking (HID) and implies the same average risks to all heavy drinkers regardless of whether they consume 5 or 15 drinks. Recent studies have raised attention to HID of consuming double (8+/10+ drinks) or even triple (12+/15+ drinks) the HED amount. As amounts per day reach extreme levels, risks for consequences and alcohol use disorders (AUD) increase greatly, even when average intake remains the same. This proposed Center project will trace trends in harmful drinking patterns with special attention to HID occurrence and rates over a 40-year period, identify a range of alcohol-related precursors and problems through event-based and population-based approaches, and situate harmful patterns within contexts to inform early screening and interventions for high-risk groups. We will employ sophisticated statistical methods to examine associations and consequences involved with HED and HID, using two adult general population and two heavy drinker event-based samples, each type of data designed to generate complementary and new knowledge about HID. We carry forward ARG's rich history in alcohol epidemiology and measurement expertise, and the Center mission to reduce alcohol-related disparities, in all our study aims. In response to NIAAA's priorities, and to ensure direct translational impact of study findings, we incorporate specific theory and practice for each study aim. Our definitions of HED/HID include both threshold (5+, 8+ and 12+) and amounts drunk in any day (from 5-7, 8-11, and 12+ drinks). Aim 1 takes a life course perspective to document harmful drinking trends over time from 1984 to 2024 and by priority groups (e.g., women; middle-aged to older adults; socially disadvantaged groups including racial/ethnic minorities and those with lower education; and people with history of AUD treatment or in recovery). Aim 2 characterizes event-level factors that heighten the risks for HID compared to HED levels. Sensitivity analyses will compare whether adjustments for drink size/strength and drinking rate (estimated BAC) will improve accuracy of how HID predict alcohol problems (e.g., AUD). Aim 3 situates harmful drinking patterns involving HID within the socioecological model to examine risk and protective factors both currently and across the life course that are strongly associated with HID. Also in Aim 3 we assess the extent that HID elevates the public health burden of AUD and other problems above that of HED and non-HED levels for all drinkers and priority groups using risk models and machine learning methods. Study findings will help identify key population subgroups and risk/protective factors that can support future development of targeted interventions at the event, individual, and environmental levels to reduce heavy and high-intensity drinking and their associated consequences.